We conduct two different investigations to make up this survey.
Figure REF  depicts the methods for both investigations.
The first addresses text-based, open-domain conversational ai in terms of architectures while the second addresses the ethical issues about the gender of such systems.
The first involves online search on Google Scholar and regular Google Search, using the term "sota open-domain dialogue systems".
This returned 5,130 and 34,100,000 items in the results for Google Scholar and Google Search, respectively.
We then sieve through the list of scientific papers (within the first ten pages because of time-constraint) to identify those that report sota results in the last five years (2017-2022) in order to give more attention to them.
It is important to note that some Google Scholar results point to other databases, like ScienceDirect, arXiv, and IEEEXplore.
The reason for also searching on regular Google Search is because it provides results that are not necessarily based on peer-reviewed publications but may be helpful in leading to peer-reviewed publications that may not have been immediately obvious on Scholar.
We did not discriminate the papers based on the field of publication, as we are interested in as many sota open-domain conversational systems as possible, within the specified period.
A second stage involves classifying, specifically, the sota open-domain conversational ai from the papers, based on their architecture.
We also consider models that are pretrained on large text and may be adapted for conversational systems, such as the t5 [1]}, and autoregressive models because they easily follow the nlg framework.
We do not consider models for which we did not find their scientific papers.
